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Record W1502274547

FPGA based stereo vision system to show video of dense disparity map

2012· article· en· W1502274547 on OpenAlexaff
Kunio Takaya, Qian Zheng

Bibliographic record

VenueSociety of Instrument and Control Engineers of Japan · 2012
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsComputer scienceComputer visionArtificial intelligenceComputer stereo visionField-programmable gate arrayVideo Graphics ArrayRaster scanComputer graphics (images)Matching (statistics)StereopsisTone mappingHigh dynamic rangeMathematicsComputer hardwareDynamic range
DOInot available

Abstract

fetched live from OpenAlex

Dense stereo disparity map displayed in real time as video is one form of 3D video that renders objects in the scene by the intensity modulation in terms of the distance to the objects. With two cameras whose the optical axes are parallel, the stereo disparity is calculated to find the range to the objects. Sophisticated matching algorithms are generally slow and difficult to apply parallelism to run in real time at a video rate. In order to realize a real time system, the window based stereo matching algorithm that searches the minimum of the sum of absolute differences along the raster scan lines was implemented in the Altera Cyclone II EP2C35F672C6 FPGA. The developed HDL code successfully achieved DDM dynamically displaying the measure of distance, dense disparity map in VGA format at 15 fps.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.223
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2012
Admission routes1
Has abstractyes

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